HD-Marker: a highly multiplexed and flexible approach for targeted genotyping of more than 10,000 genes in a single-tube assay

CRISPR/Cpf1-FOKI-induced gene editing in Gluconobacter oxydans

Gluconobacter oxydans is an important Gram-negative industrial microorganism that produces vitamin C and other products due to its efficient membrane-bound dehydrogenase system. Its incomplete oxidation system has many crucial industrial applications. However, it also leads to slow growth and low biomass, requiring further metabolic modification for balancing the cell growth and incomplete oxidation process. As a non-model strain, G. oxydans lacks efficient genome editing tools and cannot perform rapid multi-gene editing and complex metabolic network regulation. In the last 15 years, our laboratory attempted to deploy multiple CRISPR/Cas systems in different G. oxydans strains and found none of them as functional. In this study, Cpf1-based or dCpf1-based CRISPRi was constructed to explore the targeted binding ability of Cpf1, while Cpf1-FokI was deployed to study its nuclease activity. A study on Cpf1 found that the CRISPR/Cpf1 system could locate the target genes in G. oxydans but lacked the nuclease cleavage activity. Therefore, the CRISPR/Cpf1-FokI system based on FokI nuclease was constructed. Single-gene knockout with efficiency up to 100% and double-gene iterative editing were achieved in G. oxydans. Using this system, AcrVA6, the anti-CRISPR protein of G. oxydans was discovered for the first time, and efficient genome editing was realized.

Deqformer: high-definition and scalable deep learning probe design method

Target enrichment sequencing techniques are gaining widespread use in the field of genomics, prized for their economic efficiency and swift processing times. However, their success depends on the performance of probes and the evenness of sequencing depth among each probe. To accurately predict probe coverage depth, a model called Deqformer is proposed in this study. Deqformer utilizes the oligonucleotides sequence of each probe, drawing inspiration from Watson-Crick base pairing and incorporating two BERT encoders to capture the underlying information from the forward and reverse probe strands, respectively. The encoded data are combined with a feed-forward network to make precise predictions of sequencing depth. The performance of Deqformer is evaluated on four different datasets: SNP panel with 38 200 probes, lncRNA panel with 2000 probes, synthetic panel with 5899 probes and HD-Marker panel for Yesso scallop with 11 000 probes. The SNP and synthetic panels achieve impressive factor 3 of accuracy (F3acc) of 96.24% and 99.66% in 5-fold cross-validation. F3acc rates of over 87.33% and 72.56% are obtained when training on the SNP panel and evaluating performance on the lncRNA and HD-Marker datasets, respectively. Our analysis reveals that Deqformer effectively captures hybridization patterns, making it robust for accurate predictions in various scenarios. Deqformer leads to a novel perspective for probe design pipeline, aiming to enhance efficiency and effectiveness in probe design tasks.

Development of a high-throughput SNP array for sea cucumber (Apostichopus japonicus) and its application in genomic selection with MCP regularized deep neural networks

High-throughput single nucleotide polymorphism (SNP) genotyping assays are powerful tools for genetic studies and genomic breeding applications for many species. Though large numbers of SNPs have been identified in sea cucumber (Apostichopus japonicus), but, as yet, no high-throughput genotyping platform is available for this species. In this study, we designed and developed a high-throughput 24 K SNP genotyping array named HaishenSNP24K for A. japonicus, based on the multi-objective-local optimization (MOLO) algorithm and HD-Marker genotyping method. The SNP array exhibited a relatively high genotyping call rate (> 96%), genotyping accuracy (>95%) and exhibited highly polymorphic in sea cucumber populations. In addition, we also assessed its application in genomic selection (GS). Deep neural networks (DNN) that can capture the complicated interactions of genes have been proposed as a promising tool in GS for SNP-based genomic prediction of complex traits in animal breeding. To overcome the problem of over-fitting when using the HaishenSNP24K array as high-dimensional DNN input, we developed minmax concave penalty (MCP) regularization for sparse deep neural networks (DNN-MCP) that finds an optimal sparse structure of a DNN by minimizing the square error subject to the non-convex penalty MCP on the parameters (weights and biases). Compared to two linear models, namely RR-GBLUP and Bayes B, and the nonlinear model DNN, DNN-MCP has greatly improved the genomic prediction ability for three quantitative traits (e.g., wet weight, dry weight and survival time) in the sea cucumber population. To the best of our knowledge, this is the first work to develop a high-throughput SNP array for A. japonicus and a new model DNN-MCP for genomic prediction of complex traits in GS. The present results provide evidence that supports the HaishenSNP24K array with DNN-MCP will be valuable for genetic studies and molecular breeding in A. japonicus.

Genome-Wide Association Study Reveals PC4 as the Candidate Gene for Thermal Tolerance in Bay Scallop (Argopecten irradians irradians)

The increasing sea temperature caused by global warming has resulted in severe mortalities in maricultural scallops. Therefore, improving thermal tolerance has become an active research area in the scallop farming industry. Bay scallop (Argopecten irradians irradians) was introduced into China in 1982 and has developed into a vast aquaculture industry in northern China. To date, genetic studies on thermal tolerance in bay scallops are limited, and no systematic screening of thermal tolerance-related loci or genes has been conducted in this species. In the present study, we conducted a genome-wide association study (GWAS) for thermal tolerance using the Arrhenius break temperature (ABT) indicators of 435 bay scallops and 38,011 single nucleotide polymorphism (SNP) markers. The GWAS identified 1,906 significant thermal tolerance-associated SNPs located in 16 chromosomes of bay scallop. Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses showed that 638 genes were enriched in 42 GO terms, while 549 annotated genes were enriched in aggregation pathways. Additionally, the SNP (15-5091-20379557-1) with the lowest P value was located in the transcriptional coactivator p15 (PC4) gene, which is involved in regulating DNA damage repair and stabilizing genome functions. Further analysis in another population identified two new thermal tolerance-associated SNPs in the first coding sequence of PC4 in bay scallops (AiPC4). Moreover, AiPC4 expression levels were significantly correlated (r = 0.675–0.962; P < 0.05) with the ABT values of the examined bay scallops. Our data suggest that AiPC4 might be a positive regulator of thermal tolerance and a potential candidate gene for molecular breeding in bay scallop aiming at thermal tolerance improvement.

Sequencing-Based Transcriptome-Wide Targeted Genotyping for Evolutionary and Ecological Studies

Transcriptome-wide targeted genotyping is highly attractive for evolutionary and ecological studies but, until recently, accomplishing this goal presented a major technical barrier for the study of non-model organisms. Our group has recently developed a high-throughput targeted genotyping approach (called HD-Marker) based on the high specificity and accuracy of oligo extension-ligation assays that facilitates the design of assays tailored to meet specific genotyping needs. HD-Marker allows for targeted genotyping of over 10 000 genes in a single tube, with strikingly high capture rate (98%-99%) and genotyping accuracy (97%-99%). With the remarkable advantages of cost-effectiveness and flexibility, we envision that HD-Marker has broad application potential in evolutionary and ecological studies.